Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Change detection based on remote sensing images plays an important role in the field of
remote sensing analysis, and it has been widely used in many areas, such as resources …

Abstract Contrastive Language-Image Pre-training (CLIP) has made a remarkable
breakthrough in open-vocabulary zero-shot image recognition. Many recent studies …

In this paper, we study the semi-supervised semantic segmentation problem via exploring
both labeled data and extra unlabeled data. We propose a novel consistency regularization …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

Semi-supervised semantic segmentation (SSS) has recently gained increasing research
interest as it can reduce the requirement for large-scale fully-annotated training data. The …

Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress.
Despite their promising performance, current state-of-the-art methods tend to increasingly …

Generalizing the medical image segmentation algorithms to unseen domains is an important
research topic for computer-aided diagnosis and surgery. Most existing methods require a …

Due to the lack of quality annotation in medical imaging community, semi-supervised
learning methods are highly valued in image semantic segmentation tasks. In this paper, an …

Abstract Semi-Supervised Semantic Segmentation aims at training the segmentation model
with limited labeled data and a large amount of unlabeled data. To effectively leverage the …